Internet browsing across desired websites and web pages has become an essential activity for users. Moreover, engaging the users during internet browsing is a challenge. Various techniques to ensure that the users utilize web content efficiently, are improvised day-to-day. However, the techniques necessitate information related to the user's engagement with the web content.
Traditional quantitative metrics like page views, time spent and click-through-rate are applied to calculate the engagement of users with the web pages. However, such quantitative metrics do not provide enough information and insight about the user's engagement. Moreover, details of how the users interacted with the web content are overlooked. Consequently, various business users, for example editors, designers and advertisers are not empowered to take business decisions as information related to behavioral patterns of users are not evident.
In light of the foregoing discussion, there is a need for an efficient method and system for visualizing patterns during internet browsing.